Vijay K. Narayanan

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The explosion of online content has made the management of such content non-trivial. Web-related tasks such as web page categorization, news filtering, query categorization, tag recommendation, etc. often involve the construction of multi-label categorization systems on a large scale. Existing multi-label classification methods either do not scale or have(More)
We present the experiences from building a web-scale user modeling platform for optimizing display advertising targeting at Yahoo!. The platform described in this paper allows for per-campaign maximization of <i>conversions</i> representing purchase activities or transactions. Conversions directly translate to advertiser's revenue, and thus provide the most(More)
Online advertising is becoming more and more performance oriented where the decision to show an advertisement to a user is made based on the user's propensity to respond to the ad in a positive manner, (e.g., purchasing a product, subscribing to an email list). The user response depends on how well the ad campaign matches to the user's interest, as well as(More)
Recent advances in high throughput data collection and storage technologies have led to a dramatic increase in the availability of high-resolution time series data sets in various domains. These time series reflect the dynamics of the underlying physical processes in these domains. Detecting changes in a time series over time or changes in the relationships(More)
Principal Component Analysis (PCA) is a popular technique with many applications. Recent randomized PCA algorithms scale to large datasets but face a bottleneck when the number of features is also large. We propose to mitigate this issue using a composition of structured and unstructured randomness within a randomized PCA algorithm. Initial experiments(More)
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